From visual results, we can observe that DCP [1]}, CAP [2]} and Meng et al. [3]} easily suffers from color distortion, which make the brightness of several areas relatively dark. AMEF [4]}, DehazeNet [5]}, AOD-Net [6]} and DCPDN [7]} still remain haze in heavily hazy scene. The processing power of GFN [8]}, GCANet [9]} and FD-GAN [10]} at high-frequency detail information performance is unnatural. Although DuRN [11]}, GridNet [12]} and MSBDN [13]} look good in some cases, there are flaws in detail restoration. our method achieves best visual effect, which could both preserve the image detail and color as well as remove the haze as much as possible from the input. This is because our method could provide suitable scene prior. Meanwhile, our method conducts non-local filtering in the spatial and channel dimensions to model long-range dependency and propagate information between graph nodes for better dehazing optimization.
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